[MNT] Hotfix tag scitype:y typo

sktime/forecasting/tests/test_all_forecasters.py

@@ -62,8 +62,9 @@
 def test_get_fitted_params(Forecaster):
     """Test get_fitted_params."""
     f = _construct_instance(Forecaster)
-    columns = _get_n_columns(f.get_tag("y:scitype"))
+    columns = _get_n_columns(f.get_tag("scitype:y"))
     for n_columns in columns:
+        f = _construct_instance(Forecaster)
         y_train = _make_series(n_columns=n_columns)
         f.fit(y_train, fh=FH0)
         try:
@@ -130,9 +131,10 @@ def test_y_invalid_type_raises_error(Forecaster, y):
 def test_X_invalid_type_raises_error(Forecaster, X):
     """Test that invalid X input types raise error."""
     f = _construct_instance(Forecaster)
-    n_columns_list = _get_n_columns(f.get_tag("y:scitype"))
+    n_columns_list = _get_n_columns(f.get_tag("scitype:y"))
 
     for n_columns in n_columns_list:
+        f = _construct_instance(Forecaster)
         y_train = _make_series(n_columns=n_columns)
         try:
             with pytest.raises(TypeError, match=r"type"):
@@ -150,9 +152,10 @@ def test_X_invalid_type_raises_error(Forecaster, X):
 def test_predict_time_index(Forecaster, index_type, fh_type, is_relative, steps):
     """Check that predicted time index matches forecasting horizon."""
     f = _construct_instance(Forecaster)
-    n_columns_list = _get_n_columns(f.get_tag("y:scitype"))
+    n_columns_list = _get_n_columns(f.get_tag("scitype:y"))
 
     for n_columns in n_columns_list:
+        f = _construct_instance(Forecaster)
         y_train = _make_series(
             n_columns=n_columns, index_type=index_type, n_timepoints=50
         )
@@ -175,13 +178,14 @@ def test_predict_time_index(Forecaster, index_type, fh_type, is_relative, steps)
 def test_predict_time_index_with_X(Forecaster, index_type, fh_type, is_relative, steps):
     """Check that predicted time index matches forecasting horizon."""
     f = _construct_instance(Forecaster)
-    n_columns_list = _get_n_columns(f.get_tag("y:scitype"))
+    n_columns_list = _get_n_columns(f.get_tag("scitype:y"))
 
     z, X = make_forecasting_problem(index_type=index_type, make_X=True)
 
     # Some estimators may not support all time index types and fh types, hence we
     # need to catch NotImplementedErrors.
     for n_columns in n_columns_list:
+        f = _construct_instance(Forecaster)
         y = _make_series(n_columns=n_columns, index_type=index_type)
         cutoff = y.index[len(y) // 2]
         fh = _make_fh(cutoff, steps, fh_type, is_relative)
@@ -205,9 +209,10 @@ def test_predict_time_index_in_sample_full(
 ):
     """Check that predicted time index equals fh for full in-sample predictions."""
     f = _construct_instance(Forecaster)
-    n_columns_list = _get_n_columns(f.get_tag("y:scitype"))
+    n_columns_list = _get_n_columns(f.get_tag("scitype:y"))
 
     for n_columns in n_columns_list:
+        f = _construct_instance(Forecaster)
         y_train = _make_series(n_columns=n_columns, index_type=index_type)
         cutoff = y_train.index[-1]
         steps = -np.arange(len(y_train))
@@ -265,9 +270,10 @@ def test_predict_pred_interval(Forecaster, fh, alpha):
             and no NotImplementedError is raised when asking predict for pred.int
     """
     f = _construct_instance(Forecaster)
-    n_columns_list = _get_n_columns(f.get_tag("y:scitype"))
+    n_columns_list = _get_n_columns(f.get_tag("scitype:y"))
 
     for n_columns in n_columns_list:
+        f = _construct_instance(Forecaster)
         y_train = _make_series(n_columns=n_columns)
         f.fit(y_train, fh=fh)
         if f.get_tag("capability:pred_int"):
@@ -284,9 +290,10 @@ def test_predict_pred_interval(Forecaster, fh, alpha):
 def test_score(Forecaster, fh):
     """Check score method."""
     f = _construct_instance(Forecaster)
-    n_columns_list = _get_n_columns(f.get_tag("y:scitype"))
+    n_columns_list = _get_n_columns(f.get_tag("scitype:y"))
 
     for n_columns in n_columns_list:
+        f = _construct_instance(Forecaster)
         y = _make_series(n_columns=n_columns)
         y_train, y_test = temporal_train_test_split(y)
         f.fit(y_train, fh=fh)
@@ -309,9 +316,10 @@ def test_score(Forecaster, fh):
 def test_update_predict_single(Forecaster, fh, update_params):
     """Check correct time index of update-predict."""
     f = _construct_instance(Forecaster)
-    n_columns_list = _get_n_columns(f.get_tag("y:scitype"))
+    n_columns_list = _get_n_columns(f.get_tag("scitype:y"))
 
     for n_columns in n_columns_list:
+        f = _construct_instance(Forecaster)
         y = _make_series(n_columns=n_columns)
         y_train, y_test = temporal_train_test_split(y)
         f.fit(y_train, fh=fh)
@@ -323,9 +331,10 @@ def _check_update_predict_predicted_index(
     Forecaster, fh, window_length, step_length, update_params
 ):
     f = _construct_instance(Forecaster)
-    n_columns_list = _get_n_columns(f.get_tag("y:scitype"))
+    n_columns_list = _get_n_columns(f.get_tag("scitype:y"))
 
     for n_columns in n_columns_list:
+        f = _construct_instance(Forecaster)
         y_train = _make_series(
             n_columns=n_columns, all_positive=True, index_type="datetime"
         )

sktime/forecasting/tests/test_sktime_forecasters.py

@@ -55,9 +55,10 @@ def test_oh_setting(Forecaster):
     """Check cuttoff and _y."""
     # check _y and cutoff is None after construction
     f = _construct_instance(Forecaster)
-    n_columns_list = _get_n_columns(f.get_tag("y:scitype"))
+    n_columns_list = _get_n_columns(f.get_tag("scitype:y"))
 
     for n_columns in n_columns_list:
+        f = _construct_instance(Forecaster)
         y = _make_series(n_columns=n_columns)
         y_train, y_test = temporal_train_test_split(y, train_size=0.75)
 
@@ -143,9 +144,10 @@ def test_different_fh_in_fit_and_predict_req(Forecaster):
 def test_no_fh_opt(Forecaster):
     """Check if fh is optional in fit."""
     f = _construct_instance(Forecaster)
-    n_columns_list = _get_n_columns(f.get_tag("y:scitype"))
+    n_columns_list = _get_n_columns(f.get_tag("scitype:y"))
 
     for n_columns in n_columns_list:
+        f = _construct_instance(Forecaster)
         y_train = _make_series(n_columns=n_columns)
         f.fit(y_train)
         # not passing fh to either fit or predict raises error
@@ -157,11 +159,11 @@ def test_no_fh_opt(Forecaster):
 def test_fh_in_fit_opt(Forecaster):
     """Check if fh is optional in fit."""
     f = _construct_instance(Forecaster)
-    n_columns_list = _get_n_columns(f.get_tag("y:scitype"))
+    n_columns_list = _get_n_columns(f.get_tag("scitype:y"))
 
     for n_columns in n_columns_list:
-        y_train = _make_series(n_columns=n_columns)
         f = _construct_instance(Forecaster)
+        y_train = _make_series(n_columns=n_columns)
         f.fit(y_train, fh=FH0)
         np.testing.assert_array_equal(f.fh, FH0)
         f.predict()
@@ -172,12 +174,12 @@ def test_fh_in_fit_opt(Forecaster):
 def test_fh_in_predict_opt(Forecaster):
     """Check if fh is optional in predict."""
     f = _construct_instance(Forecaster)
-    n_columns_list = _get_n_columns(f.get_tag("y:scitype"))
+    n_columns_list = _get_n_columns(f.get_tag("scitype:y"))
 
     for n_columns in n_columns_list:
+        f = _construct_instance(Forecaster)
         y_train = _make_series(n_columns=n_columns)
         y_train = _make_series(n_columns=1)
-        f = _construct_instance(Forecaster)
         f.fit(y_train)
         f.predict(FH0)
         np.testing.assert_array_equal(f.fh, FH0)
@@ -187,9 +189,10 @@ def test_fh_in_predict_opt(Forecaster):
 def test_same_fh_in_fit_and_predict_opt(Forecaster):
     """Check if fh is the same in fit and predict."""
     f = _construct_instance(Forecaster)
-    n_columns_list = _get_n_columns(f.get_tag("y:scitype"))
+    n_columns_list = _get_n_columns(f.get_tag("scitype:y"))
 
     for n_columns in n_columns_list:
+        f = _construct_instance(Forecaster)
         y_train = _make_series(n_columns=n_columns)
 
         # passing the same fh to both fit and predict works

sktime/utils/_testing/forecasting.py

@@ -29,11 +29,13 @@ def _get_n_columns(tag):
         n_columns_list = [2]
     elif tag == "both":
         n_columns_list = [1, 2]
+    else:
+        raise ValueError(f"Unexpected tag {tag}.")
     return n_columns_list
 
 
 def _get_expected_index_for_update_predict(y, fh, step_length):
-    """Helper function to compute expected time index from `update_predict`"""
+    """Compute expected time index from `update_predict`."""
     # time points at which to make predictions
     fh = check_fh(fh)
     index = y.index
@@ -69,8 +71,7 @@ def _get_expected_index_for_update_predict(y, fh, step_length):
 
 
 def _generate_polynomial_series(n, order, coefs=None):
-    """Helper function to generate polynomial series of given order and
-    coefficients"""
+    """Generate polynomial series of given order and coefficients."""
     if coefs is None:
         coefs = np.ones((order + 1, 1))
     x = np.vander(np.arange(n), N=order + 1).dot(coefs)
@@ -85,6 +86,29 @@ def make_forecasting_problem(
     n_columns=2,
     random_state=None,
 ):
+    """Return test data for forecasting tests.
+
+    Parameters
+    ----------
+    n_timepoints : int, optional
+        Lenght of data, by default 50
+    all_positive : bool, optional
+        Only positive values or not, by default True
+    index_type : e.g. pd.PeriodIndex, optional
+        pandas Index type, by default None
+    make_X : bool, optional
+        Should X data also be returned, by default False
+    n_columns : int, optional
+        Number of columns of X, by default 2
+    random_state : inst, str, float, optional
+        Set seed of random state, by default None
+
+    Returns
+    -------
+    ps.Series, pd.DataFrame
+        y, if not make_X
+        y, X if make_X
+    """
     y = _make_series(
         n_timepoints=n_timepoints,
         n_columns=1,
@@ -115,7 +139,7 @@ def _assert_correct_pred_time_index(y_pred_index, cutoff, fh):
 
 
 def _make_fh(cutoff, steps, fh_type, is_relative):
-    """Helper function to construct forecasting horizons for testing"""
+    """Construct forecasting horizons for testing."""
     from sktime.forecasting.tests._config import INDEX_TYPE_LOOKUP
 
     fh_class = INDEX_TYPE_LOOKUP[fh_type]